Fraud Models in the Energy Sector - Using AI for Smart Meter Theft Detection

Overview

Explore AI-driven fraud detection for smart meters in the energy sector, focusing on energy theft prevention and consumption monitoring techniques.

Syllabus

• Introduction to Fraud Detection in the Energy Sector
Overview of the energy sector’s landscape
Common types of fraud and their impact
Importance of fraud detection for smart meters
• Fundamentals of Artificial Intelligence
Basics of machine learning and AI
Key algorithms and techniques: classification, clustering, anomaly detection
Introduction to neural networks and deep learning
• Smart Meters and Data Collection
Structure and functionality of smart meters
Types of data collected by smart meters
Data privacy and ethical considerations
• Energy Theft Detection Techniques
Identifying patterns indicative of energy theft
Common algorithms used for theft detection
Case studies of successful detection implementations
• AI Models for Fraud Detection
Anomaly detection models: autoencoders, isolation forests
Supervised vs. unsupervised learning
Feature engineering specific to energy consumption data
• Implementing AI-driven Solutions
Data preprocessing and cleansing techniques
Model training and evaluation
Handling imbalanced datasets
• Tools and Technologies
Introduction to popular tools: TensorFlow, PyTorch, Scikit-learn
Leveraging cloud platforms for large-scale data processing and model deployment
Infrastructure considerations for real-time fraud detection
• Monitoring and Maintenance of AI Systems
Ensuring model robustness and accuracy over time
Adapting models to evolving patterns of fraud
Designing human-in-the-loop feedback systems
• Case Studies and Industry Applications
Analysis of recent case studies in energy theft detection
Lessons learned from deployment and scaling of AI systems in the sector
Future trends in AI and fraud detection in the energy industry
• Capstone Project
Design and implement a prototype AI system for smart meter theft detection
Presentation and critique of project findings and methodologies

---

Subjects

Data Science